Multi-objective Optimization of Stirling Heat Engine Using Gray Wolf Optimization Algorithm

The use of meta-heuristic optimization methods have become quite generic in the past two decades. This paper provides a theoretical investigation to find optimum design parameters of the Stirling heat engines using a recently presented nature-inspired method namely the gray wolf optimization (GWO). This algorithm is utilized for the maximization of the output power/thermal efficiency as well as minimization of the pressure loss. The linear programming technique is employed for analyzing the multi-objective problem and the result is compared with the three individually computed costs of the aforementioned cost functions. The results show that the new meta-heuristic algorithm (i.e. GWO) yields acceptable results in quality compared to the other presented methods such as TOPSIS and Bellman-Zadeh.